/////////////////////////////////////////// // Running ctGAN on folding_abalone9-18 /////////////////////////////////////////// Load 'data_input/folding_abalone9-18' from pickle file Data loaded. -> Shuffling data ### Start exercise for synthetic point generator ====== Step 1/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 1/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.464 LR average precision score: 0.554 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 4, 5 GB f1 score: 0.556 GB cohens kappa score: 0.527 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 7, 2 LR f1 score: 0.308 LR cohens kappa score: 0.281 LR average precision score: 0.363 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 8, 1 KNN f1 score: 0.154 KNN cohens kappa score: 0.121 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 8, 1 LR f1 score: 0.111 LR cohens kappa score: 0.053 LR average precision score: 0.072 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 7, 2 GB f1 score: 0.286 GB cohens kappa score: 0.253 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 5, 4 LR f1 score: 0.444 LR cohens kappa score: 0.408 LR average precision score: 0.390 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 7, 2 GB f1 score: 0.333 GB cohens kappa score: 0.312 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 7, 2 KNN f1 score: 0.333 KNN cohens kappa score: 0.312 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 134, 3 LR fn, tp: 5, 1 LR f1 score: 0.200 LR cohens kappa score: 0.172 LR average precision score: 0.159 -> test with 'GB' GB tn, fp: 137, 0 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 6, 3 LR f1 score: 0.429 LR cohens kappa score: 0.402 LR average precision score: 0.470 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 7, 2 GB f1 score: 0.333 GB cohens kappa score: 0.312 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 8, 1 KNN f1 score: 0.182 KNN cohens kappa score: 0.163 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 125, 13 LR fn, tp: 7, 2 LR f1 score: 0.167 LR cohens kappa score: 0.098 LR average precision score: 0.234 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 8, 1 LR f1 score: 0.118 LR cohens kappa score: 0.064 LR average precision score: 0.089 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 7, 2 GB f1 score: 0.267 GB cohens kappa score: 0.229 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 7, 2 KNN f1 score: 0.286 KNN cohens kappa score: 0.253 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 128, 10 LR fn, tp: 3, 6 LR f1 score: 0.480 LR cohens kappa score: 0.436 LR average precision score: 0.651 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 5, 4 GB f1 score: 0.500 GB cohens kappa score: 0.472 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 7, 2 KNN f1 score: 0.333 KNN cohens kappa score: 0.312 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 126, 11 LR fn, tp: 3, 3 LR f1 score: 0.300 LR cohens kappa score: 0.256 LR average precision score: 0.205 -> test with 'GB' GB tn, fp: 129, 8 GB fn, tp: 4, 2 GB f1 score: 0.250 GB cohens kappa score: 0.208 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 6, 3 LR f1 score: 0.333 LR cohens kappa score: 0.290 LR average precision score: 0.398 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 8, 1 GB f1 score: 0.154 GB cohens kappa score: 0.121 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 123, 15 LR fn, tp: 7, 2 LR f1 score: 0.154 LR cohens kappa score: 0.080 LR average precision score: 0.103 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 137, 1 LR fn, tp: 5, 4 LR f1 score: 0.571 LR cohens kappa score: 0.552 LR average precision score: 0.617 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.281 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 4, 5 LR f1 score: 0.476 LR cohens kappa score: 0.437 LR average precision score: 0.584 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 4, 5 GB f1 score: 0.526 GB cohens kappa score: 0.494 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 6, 3 KNN f1 score: 0.500 KNN cohens kappa score: 0.484 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 120, 17 LR fn, tp: 5, 1 LR f1 score: 0.083 LR cohens kappa score: 0.022 LR average precision score: 0.224 -> test with 'GB' GB tn, fp: 133, 4 GB fn, tp: 5, 1 GB f1 score: 0.182 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 133, 4 KNN fn, tp: 5, 1 KNN f1 score: 0.182 KNN cohens kappa score: 0.149 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 5, 4 LR f1 score: 0.400 LR cohens kappa score: 0.357 LR average precision score: 0.514 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 7, 2 KNN f1 score: 0.364 KNN cohens kappa score: 0.349 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 119, 19 LR fn, tp: 4, 5 LR f1 score: 0.303 LR cohens kappa score: 0.235 LR average precision score: 0.394 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 7, 2 KNN f1 score: 0.333 KNN cohens kappa score: 0.312 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 137, 1 LR fn, tp: 5, 4 LR f1 score: 0.571 LR cohens kappa score: 0.552 LR average precision score: 0.580 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 6, 3 GB f1 score: 0.353 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 111, 27 LR fn, tp: 5, 4 LR f1 score: 0.200 LR cohens kappa score: 0.116 LR average precision score: 0.204 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.281 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 131, 6 LR fn, tp: 3, 3 LR f1 score: 0.400 LR cohens kappa score: 0.368 LR average precision score: 0.361 -> test with 'GB' GB tn, fp: 130, 7 GB fn, tp: 5, 1 GB f1 score: 0.143 GB cohens kappa score: 0.100 -> test with 'KNN' KNN tn, fp: 136, 1 KNN fn, tp: 5, 1 KNN f1 score: 0.250 KNN cohens kappa score: 0.234 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 124, 14 LR fn, tp: 9, 0 LR f1 score: 0.000 LR cohens kappa score: -0.081 LR average precision score: 0.071 -> test with 'GB' GB tn, fp: 129, 9 GB fn, tp: 8, 1 GB f1 score: 0.105 GB cohens kappa score: 0.044 -> test with 'KNN' KNN tn, fp: 134, 4 KNN fn, tp: 8, 1 KNN f1 score: 0.143 KNN cohens kappa score: 0.104 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 136, 2 LR fn, tp: 5, 4 LR f1 score: 0.533 LR cohens kappa score: 0.509 LR average precision score: 0.588 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 7, 2 GB f1 score: 0.286 GB cohens kappa score: 0.253 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 96, 42 LR fn, tp: 2, 7 LR f1 score: 0.241 LR cohens kappa score: 0.154 LR average precision score: 0.306 -> test with 'GB' GB tn, fp: 133, 5 GB fn, tp: 7, 2 GB f1 score: 0.250 GB cohens kappa score: 0.208 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 7, 2 KNN f1 score: 0.286 KNN cohens kappa score: 0.253 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 5, 4 LR f1 score: 0.333 LR cohens kappa score: 0.278 LR average precision score: 0.408 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 6, 3 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 7, 2 KNN f1 score: 0.364 KNN cohens kappa score: 0.349 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> test with 'LR' LR tn, fp: 126, 11 LR fn, tp: 4, 2 LR f1 score: 0.211 LR cohens kappa score: 0.162 LR average precision score: 0.304 -> test with 'GB' GB tn, fp: 137, 0 GB fn, tp: 3, 3 GB f1 score: 0.667 GB cohens kappa score: 0.657 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 137, 42 LR fn, tp: 9, 7 LR f1 score: 0.571 LR cohens kappa score: 0.552 LR average precision score: 0.651 average: LR tn, fp: 127.68, 10.12 LR fn, tp: 5.2, 3.2 LR f1 score: 0.315 LR cohens kappa score: 0.267 LR average precision score: 0.354 minimum: LR tn, fp: 96, 1 LR fn, tp: 2, 0 LR f1 score: 0.000 LR cohens kappa score: -0.081 LR average precision score: 0.071 -----[ GB ]----- maximum: GB tn, fp: 137, 9 GB fn, tp: 8, 5 GB f1 score: 0.667 GB cohens kappa score: 0.657 average: GB tn, fp: 134.12, 3.68 GB fn, tp: 6.0, 2.4 GB f1 score: 0.324 GB cohens kappa score: 0.292 minimum: GB tn, fp: 129, 0 GB fn, tp: 3, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -----[ KNN ]----- maximum: KNN tn, fp: 138, 4 KNN fn, tp: 9, 3 KNN f1 score: 0.500 KNN cohens kappa score: 0.484 average: KNN tn, fp: 136.72, 1.08 KNN fn, tp: 7.24, 1.16 KNN f1 score: 0.213 KNN cohens kappa score: 0.196 minimum: KNN tn, fp: 133, 0 KNN fn, tp: 5, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023